AutoHedge
Deploying AutoHedge for evaluation: isolate dependencies, history and signing authority
Prepare a reproducible research-only evaluation environment, identify configuration drift and understand why an installation is not a verified live-trading deployment.
What you will learn
- Build a reproducible research environment
- Resolve configuration from the code
- Identify state and rollback boundaries
Before you start
- Basic Python, Git and dependency-management knowledge
- A fictional evidence task with no wallet or signing authority
Explain the inspected implementation and its counterexamples without mistaking a simulation or generated text for a verified financial outcome.
Key takeaways
- Pin the source and record resolved dependencies; wildcard declarations are not a lock.
- Parent .env discovery and plain-text history need an explicit boundary.
- Keep research evaluation separate from signing credentials and tools.
Build a reproducible research environment
The inspected pyproject uses Python ^3.10 and declares many runtime dependencies with wildcard versions. The repository tree has no dependency lockfile. Installing the same AutoHedge version on different dates can therefore resolve different Swarms, HTTP and signing libraries. Capture the interpreter, package versions and commit alongside any evaluation result.
The reference commands below show a fresh checkout and isolated virtual environment, but were not executed here. Installing dependencies requires network access and dependency review. The commands intentionally stop before launching AutoHedge: the package import constructs workers, and no documented CLI paper-trading switch was established from this source.
Resolve configuration from the code
find_project_env walks from the current working directory up through its parents and chooses the first .env file. That search does not stop at a Git root. load_env passes override=False for the discovered file, so existing process variables are retained. Run from a controlled directory and provide only the credentials required for an explicitly authorized research test.
The CLI checks whether OPENAI_API_KEY is truthy and emits a warning if it is absent; it does not validate the credential. Sentiment search separately requires EXA_API_KEY. The README names WALLET_PRIVATE_KEY, while the actual Jupiter signing helper reads SOLANA_PRIVATE_KEY. For this research-only evaluation, provide neither signing variable and do not attach swap tools.
Identify state and rollback boundaries
The wrapper creates its output directory with mkdir(exist_ok=True), without parents=True. A missing intermediate parent can fail. output_file_path is stored but is not written by the inspected run method; it is not an implemented persistence guarantee. Separately, the CLI keeps plain-text recent tasks under the user’s home directory.
Record a clean environment and intentional state locations before any authorized test, and retain the previous package environment for rollback. Acceptance requires the expected research artifact and observed provider behavior, not just successful installation. This chapter does not establish a daemon, hardened multi-user service or production financial execution system.
Implementation steps
- 1
Create a fresh checkout and dedicated virtual environment.
- 2
Review the dependency set and choose controlled state paths.
- 3
Do not provide wallet keys or connect signing functions.
- 4
Only run an authorized research test after import and provider review.
Copy-ready example
git clone https://github.com/The-Swarm-Corporation/AutoHedge.git autohedge-evaluation
cd autohedge-evaluation
git checkout --detach c549c7950da112286e76725d49f6a25de8fa99bd
python -m venv .venv
# Activate the environment using your platform-specific command.
python -m pip install .
python -m pip freezeFrequently asked questions
Do these commands start trading?
They are unexecuted installation references and do not launch the application. Dependency installation still requires review; do not add signing keys or tools for a research-only test.
Does output_file_path save the research report?
The inspected class stores that value but its run method does not write the named file.
Sources
- pyproject.tomlSource checked 2026-09-08
- requirements.txtSource checked 2026-09-08
- autohedge/main.pySource checked 2026-09-08
- autohedge/workers.pySource checked 2026-09-08
- autohedge/cli.pySource checked 2026-09-08
- autohedge/env_loader.pySource checked 2026-09-08
- autohedge/tools/ultra_tools.pySource checked 2026-09-08
- README.mdSource checked 2026-09-08
- autohedge/__init__.pySource checked 2026-09-08